Treatment with the second-generation antipsychotic quetiapine is associated with increased subgenual ACC activation during reward processing in major depressive disorder

Omlor, Nicola; Richter, Maike; Goltermann, Janik; Steinmann, Lavinia A; Kraus, Anna; Borgers, Tiana; Klug, Melissa; Enneking, Verena; Redlich, Ronny; Dohm, Katharina; Repple, Jonathan; Leehr, Elisabeth J.; Grotegerd, Dominik; Kugel, Harald; Bauer, Jochen; Dannlowski, Udo

doi:10.1016/j.jad.2023.02.102

Cited by 2 publications

(2 citation statements)

References 72 publications

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“…Using the GPower 3.1 program [9], the sample size for each group is 10 (α = 0.05 and power = 0.85, β = 0.2). Dataset taken from the kaggle website [10]. The datasets taken from the sample Python and OpenCV [11].…”

Section: Methodsmentioning

confidence: 99%

Human Activity Recognition on Smartphones using Innovative Logistic Regression and Comparing Accuracy of Extra Gradient Boost Algorithm

Reddy,

Padmakala

2024

E3S Web Conf.

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This work uses innovative logistic regression and extra gradient boost to compare and enhance human activity recognition for walking and sitting.Novel logistic regression and Extra Gradient Boost are applied with distinct training and testing splits to predict human activity identification.From each group, ten sets of samples are selected, yielding a total of twenty samples. About 85% of the Gpower test (g power setup parameters: α=0.05 and power=0.85, ß=0.2) comes from a T test on an independent sample.Compared to Extra Gradient Boost (90.1850%), Innovative logistic regression (95.5680%) has higher accuracy, with a statistically significant value of p = 0.001 (p < 0.05). When compared to Extra Gradient Boost, Innovative logistic regression has higher accuracy.

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Section: Methodsmentioning

confidence: 99%

Human Activity Recognition on Smartphones using Innovative Logistic Regression and Comparing Accuracy of Extra Gradient Boost Algorithm

Reddy,

Padmakala

2024

E3S Web Conf.

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“…It is built on probability models that make numerous assumptions regarding how independently occurring objects are. The independence assumptions typically have no impact on reality [13]. As a result, they are viewed as being naive.…”

Section: Naive Bayesmentioning

confidence: 99%

Human Activity Recognition on Smartphones using Innovative Logistic Regression and Comparing Accuracy of Naive Bayes Algorithm

Reddy,

Sadagopan

2024

E3S Web Conf.

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The objective of this study is to compare the Naive Bayes algorithm with Innovative Logistic Regression in order to enhance human activity identification for sitting and walking. To predict human activity, Naive Bayes and Innovative Logistic Regression are used with different training and testing splits. From each group, ten sets of samples are selected, yielding a total of twenty samples. About 80% of the data from an independent sample T test were utilized in the Gpower test (g power setup parameters: α = 0.05 and power = 0.80, β = 0.2). Compared to Naive Bayes (90.7210%), Innovative Logistic Regression (95.5680%) has higher accuracy, with a statistical significance value of P = 0.003 (p < 0.05). When compared to Naive Bayes, Innovative Logistic Regression has higher accuracy.

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Treatment with the second-generation antipsychotic quetiapine is associated with increased subgenual ACC activation during reward processing in major depressive disorder

Cited by 2 publications

References 72 publications

Human Activity Recognition on Smartphones using Innovative Logistic Regression and Comparing Accuracy of Extra Gradient Boost Algorithm

Human Activity Recognition on Smartphones using Innovative Logistic Regression and Comparing Accuracy of Extra Gradient Boost Algorithm

Human Activity Recognition on Smartphones using Innovative Logistic Regression and Comparing Accuracy of Naive Bayes Algorithm

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